






'''simple docstring'''





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers.models.esm.modeling_esmfold import EsmForProteinFolding


class  snake_case__      :




					def __init__(  self  :		Tuple				, _A  :		List[Any]				, _A  :		Dict=13				, _A  :		List[str]=7				, _A  :		Optional[int]=False				, _A  :		Dict=True				, _A  :		Union[str, Any]=False				, _A  :		Dict=False				, _A  :		Tuple=19				, _A  :		int=32				, _A  :		Dict=5				, _A  :		Any=4				, _A  :		List[Any]=37				, _A  :		int="gelu"				, _A  :		Dict=0.1				, _A  :		List[str]=0.1				, _A  :		List[str]=5_12				, _A  :		List[Any]=16				, _A  :		Any=2				, _A  :		str=0.02				, _A  :		str=3				, _A  :		int=4				, _A  :		Any=None				, )    ->							int:
								UpperCAmelCase_      :       List[str]          =       parent
								UpperCAmelCase_      :       int          =       batch_size
								UpperCAmelCase_      :       Union[str, Any]          =       seq_length
								UpperCAmelCase_      :       List[str]          =       is_training
								UpperCAmelCase_      :       Optional[int]          =       use_input_mask
								UpperCAmelCase_      :       Dict          =       use_token_type_ids
								UpperCAmelCase_      :       Dict          =       use_labels
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       List[str]          =       hidden_size
								UpperCAmelCase_      :       Optional[Any]          =       num_hidden_layers
								UpperCAmelCase_      :       List[Any]          =       num_attention_heads
								UpperCAmelCase_      :       Any          =       intermediate_size
								UpperCAmelCase_      :       List[str]          =       hidden_act
								UpperCAmelCase_      :       Optional[int]          =       hidden_dropout_prob
								UpperCAmelCase_      :       Dict          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       Tuple          =       max_position_embeddings
								UpperCAmelCase_      :       List[str]          =       type_vocab_size
								UpperCAmelCase_      :       Optional[Any]          =       type_sequence_label_size
								UpperCAmelCase_      :       Optional[int]          =       initializer_range
								UpperCAmelCase_      :       List[Any]          =       num_labels
								UpperCAmelCase_      :       List[Any]          =       num_choices
								UpperCAmelCase_      :       Tuple          =       scope




					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								UpperCAmelCase_      :       int          =       ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size      )

								UpperCAmelCase_      :       List[str]          =       None
								if self.use_input_mask:
											UpperCAmelCase_      :       str          =       random_attention_mask([self.batch_size, self.seq_length]      )

								UpperCAmelCase_      :       List[str]          =       None
								UpperCAmelCase_      :       str          =       None
								UpperCAmelCase_      :       Dict          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Any          =       ids_tensor([self.batch_size]				, self.type_sequence_label_size      )
											UpperCAmelCase_      :       Optional[Any]          =       ids_tensor([self.batch_size, self.seq_length]				, self.num_labels      )
											UpperCAmelCase_      :       Tuple          =       ids_tensor([self.batch_size]				, self.num_choices      )

								UpperCAmelCase_      :       Tuple          =       self.get_config()

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								UpperCAmelCase_      :       Dict          =       EsmConfig(
								    vocab_size=33				, hidden_size=self.hidden_size				, pad_token_id=1				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, initializer_range=self.initializer_range				, is_folding_model=_A				, esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}				, )
								return config




					def 		A      (  self  :		str				, _A  :		Optional[int]				, _A  :		List[Any]				, _A  :		List[Any]				, _A  :		Tuple				, _A  :		Tuple				, _A  :		str      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Any          =       EsmForProteinFolding(config=_A      ).float()
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       List[Any]          =       model(_A				, attention_mask=_A      )
								UpperCAmelCase_      :       Dict          =       model(_A      )
								UpperCAmelCase_      :       str          =       model(_A      )

								self.parent.assertEqual(result.positions.shape				, (8, self.batch_size, self.seq_length, 14, 3)      )
								self.parent.assertEqual(result.angles.shape				, (8, self.batch_size, self.seq_length, 7, 2)      )





					def 		A      (  self  :		Optional[int]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       List[str]          =       self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      
								)      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       Dict          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					False

					a_        =					(EsmForProteinFolding,) if is_torch_available() else ()
					a_        =					()
					a_        =					{} if is_torch_available() else {}
					a_        =					False




					def 		A      (  self  :		Any      )    ->							Tuple:
								UpperCAmelCase_      :       Dict          =       EsmFoldModelTester(self      )
								UpperCAmelCase_      :       List[Any]          =       ConfigTester(self				, config_class=_A				, hidden_size=37      )




					def 		A      (  self  :		Tuple      )    ->							Any:
								self.config_tester.run_common_tests()




					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					@unittest.skip('''Does not support attention outputs'''      )
					def 		A      (  self  :		Optional[int]      )    ->							int:
								pass




					@unittest.skip
					def 		A      (  self  :		str      )    ->							Tuple:
								pass




					@unittest.skip('''Esm does not support embedding resizing'''      )
					def 		A      (  self  :		Optional[int]      )    ->							Dict:
								pass




					@unittest.skip('''Esm does not support embedding resizing'''      )
					def 		A      (  self  :		List[str]      )    ->							Any:
								pass




					@unittest.skip('''ESMFold does not support passing input embeds!'''      )
					def 		A      (  self  :		Optional[Any]      )    ->							int:
								pass




					@unittest.skip('''ESMFold does not support head pruning.'''      )
					def 		A      (  self  :		Tuple      )    ->							List[str]:
								pass




					@unittest.skip('''ESMFold does not support head pruning.'''      )
					def 		A      (  self  :		str      )    ->							List[Any]:
								pass




					@unittest.skip('''ESMFold does not support head pruning.'''      )
					def 		A      (  self  :		Tuple      )    ->							Any:
								pass




					@unittest.skip('''ESMFold does not support head pruning.'''      )
					def 		A      (  self  :		List[str]      )    ->							Tuple:
								pass




					@unittest.skip('''ESMFold does not support head pruning.'''      )
					def 		A      (  self  :		List[Any]      )    ->							int:
								pass




					@unittest.skip('''ESMFold does not output hidden states in the normal way.'''      )
					def 		A      (  self  :		List[str]      )    ->							List[str]:
								pass




					@unittest.skip('''ESMfold does not output hidden states in the normal way.'''      )
					def 		A      (  self  :		Any      )    ->							List[str]:
								pass




					@unittest.skip('''ESMFold only has one output format.'''      )
					def 		A      (  self  :		List[Any]      )    ->							int:
								pass




					@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''      )
					def 		A      (  self  :		Dict      )    ->							Dict:
								pass




					@unittest.skip('''ESMFold does not support input chunking.'''      )
					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								pass




					@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''      )
					def 		A      (  self  :		Optional[Any]      )    ->							Union[str, Any]:
								pass




					@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''      )
					def 		A      (  self  :		List[Any]      )    ->							Union[str, Any]:
								pass




					@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''      )
					def 		A      (  self  :		List[str]      )    ->							Optional[Any]:
								pass




					@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''      )
					def 		A      (  self  :		Optional[int]      )    ->							Any:
								pass




					@unittest.skip('''ESMFold doesn\'t support data parallel.'''      )
					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								pass





					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
					def 		A      (  self  :		Optional[Any]      )    ->							List[Any]:
								pass






@require_torch
class  snake_case__      (					UpperCamelCase):




					@slow
					def 		A      (  self  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       Any          =       EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''      ).float()
								model.eval()
								UpperCAmelCase_      :       Any          =       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
								UpperCAmelCase_      :       Tuple          =       model(_A      )['''positions''']
								UpperCAmelCase_      :       Optional[Any]          =       torch.tensor([2.5_828, 0.7_993, -10.9_334]				, dtype=torch.floataa      )
								self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]				, _A				, atol=1e-4      )      )










'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   List[Any]:
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4E_00 and cp <= 0X9F_FF)
			    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
			    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
			    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
			    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
			    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
			    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
			    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
			):  #
						return True

			return False
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[Any]:
			# word like '180' or '身高' or '神'
			for char in word:
						UpperCAmelCase_      :       str          =       ord(A		)
						if not _is_chinese_char(A		):
									return 0
			return 1
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Dict:
			UpperCAmelCase_      :       Optional[Any]          =       set()

			for token in tokens:
						UpperCAmelCase_      :       str          =       len(A		) > 1 and is_chinese(A		)
						if chinese_word:
									word_set.add(A		)
			UpperCAmelCase_      :       Optional[int]          =       list(A		)
			return word_list
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			set()		)    ->   Optional[Any]:
			if not chinese_word_set:
						return bert_tokens
			UpperCAmelCase_      :       Dict          =       max([len(A		) for w in chinese_word_set]		)

			UpperCAmelCase_      :       List[str]          =       bert_tokens
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, len(A		)
			while start < end:
						UpperCAmelCase_      :       str          =       True
						if is_chinese(bert_word[start]		):
									UpperCAmelCase_      :       str          =       min(end - start			,       A		)
									for i in range(A			,       1			,       -1		):
												UpperCAmelCase_      :       Tuple          =       ''''''.join(bert_word[start : start + i]		)
												if whole_word in chinese_word_set:
															for j in range(start + 1			,       start + i		):
																		UpperCAmelCase_      :       Union[str, Any]          =       '''##''' + bert_word[j]
															UpperCAmelCase_      :       Any          =       start + i
															UpperCAmelCase_      :       Optional[int]          =       False
															break
						if single_word:
									start += 1
			return bert_word
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			LTP			,       A					:			BertTokenizer		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       []

			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       int          =       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]			,       tasks=['''cws''']		).cws
						UpperCAmelCase_      :       Any          =       [get_chinese_word(A		) for r in res]
						ltp_res.extend(A		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Tuple          =       []
			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       Optional[int]          =       bert_tokenizer(lines[i : i + 1_0_0]			,       add_special_tokens=A			,       truncation=A			,       max_length=5_1_2		)
						bert_res.extend(res['''input_ids''']		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Any          =       []
			for input_ids, chinese_word in zip(A			,       A		):
						UpperCAmelCase_      :       Union[str, Any]          =       []
						for id in input_ids:
									UpperCAmelCase_      :       Union[str, Any]          =       bert_tokenizer._convert_id_to_token(A		)
									input_tokens.append(A		)
						UpperCAmelCase_      :       List[str]          =       add_sub_symbol(A			,       A		)
						UpperCAmelCase_      :       Any          =       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(A		):
									if token[:2] == "##":
												UpperCAmelCase_      :       int          =       token[2:]
												# save chinese tokens' pos
												if len(A		) == 1 and _is_chinese_char(ord(A		)		):
															ref_id.append(A		)
						ref_ids.append(A		)

			assert len(A		) == len(A		)

			return ref_ids
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:
			# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
			# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
			with open(args.file_name			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
			UpperCAmelCase_      :       List[str]          =       [line.strip() for line in data if len(A		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			UpperCAmelCase_      :       Tuple          =       LTP(args.ltp		)  # faster in GPU device
			UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(args.bert		)

			UpperCAmelCase_      :       Optional[int]          =       prepare_ref(A			,       A			,       A		)

			with open(args.save_path			,       '''w'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       str          =       [json.dumps(A		) + '''\n''' for ref in ref_ids]
						f.writelines(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser(description='prepare_chinese_ref')
						parser.add_argument(
						    '--file_name',
						    required=False,
						    type=str,
						    default='./resources/chinese-demo.txt',
						    help='file need process, same as training data in lm',
						)
						parser.add_argument(
						    '--ltp',
						    required=False,
						    type=str,
						    default='./resources/ltp',
						    help='resources for LTP tokenizer, usually a path',
						)
						parser.add_argument(
						    '--bert',
						    required=False,
						    type=str,
						    default='./resources/robert',
						    help='resources for Bert tokenizer',
						)
						parser.add_argument(
						    '--save_path',
						    required=False,
						    type=str,
						    default='./resources/ref.txt',
						    help='path to save res',
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						main(args)


